Previously, measurement of analyte levels in an individual was inconvenient and time consuming. For example, monitoring blood glucose levels in a diabetic formerly required a diabetic subject to obtain a blood sample, test that sample using e.g., a HemoCue® (Aktiebolaget Leo, Helsingborg, Sweden) clinical analyzer. Such methods typically require a finger-stick for each measurement. As a result, testing of blood glucose levels was seldom performed more frequently than 2 times per day. Such testing does not provide a complete picture of the fluctuations of glucose levels. In addition, unless the subject manually recorded the blood glucose level measured, that information was preserved only in the subject's memory. Further information regarding other factors that could affect the user's blood glucose levels, such as food intake, physical activity, etc., were similarly lost. Written records were reliable only to the extent the subject was diligent about recording information accurately and consistency. Hence, formulation of a database comprising (1) data points (e.g., measured analyte levels) and (2) information associated with each data point (e.g., the date and time of the measurement; activities affecting analyte levels such as food or water intake or physical exertion; and drug administration), was previously difficult. Further, even if obtainable, the accuracy and precision of such databases was suspect.
The present invention provides a novel method of formulating analyte data databases, analyte data databases themselves, and methods of manipulating the analyte data databases to produce useful information, e.g., for analyzing factors suspected of affecting analyte levels and investigating the efficacy of drug action for a large number of experimental subjects.